oc-p09-street-vision-poc/app

Model Comparison Dashboard

📘 This documentation is also available in French 🇫🇷

Compliance with Requirements

Exploratory Analysis of Unstructured Data (Images)

Display of image examples by category:

• Visualization of Cityscapes dataset images organized by 8 segmentation meta-classes
• Interactive selection of training and test images with preview
• Category navigation interface with user-friendly display names

Data counting and distribution:

• Pie charts detailing the distribution of the 25,000 Cityscapes images
• Visualization of project usage: 80% train / 20% validation / 100% test
• Class distribution chart for the training dataset
• Summary tables with detailed statistics per split

Image transformations:

• Interactive demonstration of preprocessing: resizing and normalization
• Visualization of random data augmentations (rotation, flip, brightness, etc.)
• Before/after comparison of transformations with visual alignment
• Complete documentation of the 6 possible augmentation types

Input Data Selection

Multiple selection interface:

• Dropdown list for Cityscapes test images with ground truth information
• Upload of personal files (JPEG, PNG) with format validation
• Preview of selected images before processing
• Error handling and real-time user feedback

Prediction Results

Full model comparison:

• Side-by-side display of predictions from the two best models
• Visualizations with original image, predicted masks, and ground truth
• Detection and highlighting of segmentation errors
• Detailed per-class statistics with percentages and pixel counts

Performance metrics:

• Comparative inference time with speed analysis
• Performance scores (mIoU, Accuracy) per model
• Per-class IoU for detailed performance analysis
• Interactive comparative result charts

Cloud Deployment

Deployed architecture:

• FastAPI application for the model comparison API
• Streamlit interface for the user dashboard
• Deployment on cloud infrastructure with high availability
• Automatic resource management and scaling

WCAG Accessibility

Compliance with essential criteria:

Color Contrast (WCAG AA)

• Color palette with contrast ratios ≥ 4.1:1 for graphical elements
• Contrast ratios ≥ 4.5:1 for text on background
• 8 main colors validated: blue (#0066CC), red (#CC0000), green (#006600), etc.
• Replacement of gradients with solid, high-contrast colors

Text Alternatives

• Detailed alternative descriptions for all automatically generated charts
• Explanatory text for each visualization (pie charts, bar charts)
• Appropriate ARIA labels for interactive elements
• Complete textual documentation of image transformations

Non-Color-Only Indication

• Patterns and hatching complementary to colors (optional via checkbox)
• 6 different patterns: diagonal, vertical, horizontal, dots, cross
• Differentiation by shape and texture in addition to colors
• Reinforced borders and outlines for visual distinction

Keyboard Navigation

• Visible CSS focus with 2px blue outlines (#0066CC)
• Full support for tab navigation
• Clear visual indicators for focused elements
• Compliance with logical navigation order

Accessible Data Tables

• Structured tables complementary to all charts
• Appropriate headers with scope and descriptions
• Exportable data readable by screen readers
• Responsive configuration for all screens

Built-in assistive features:

• "Enable patterns (visual accessibility)" checkbox for customization
• Automatic chart descriptions with complete statistics
• Interface adaptable to the needs of users with disabilities
• Complete documentation of accessibility features via the /accessibility-info endpoint

Technical Architecture

Technology stack:

• Backend: FastAPI + PyTorch for model inference
• Frontend: Streamlit with accessible components
• Visualizations: Matplotlib with WCAG palettes
• REST API documented with Pydantic validation
• Optimized state management for model comparisons

Performance and reliability:

• Optimized inference times (< 1 second per image)
• Efficient memory management with automatic figure closing
• File format and size validation
• Automatic cleanup of temporary files